FAIRLYZ is a collaborative data management platform guiding researchers to share well-annotated quality data. FAIRLYZ is available in two versions:
- A Private Metadata Commons for AI/ML Data Curation.
- A Public Global Metadata Registry, as described below.
Empowering scientists worldwide, the FAIRLYZ registry is a global platform that promotes biomedical data sharing, igniting collaboration and fundraising opportunities around data reuse. While countless scientists have dedicated their careers to groundbreaking discoveries, as medicine enters the era of AI, there are significant obstacles to overcome. Scientists who generate data do not have tools to share their data, and scientists looking for data lack access to the data. Even with the 2023 NIH data sharing policy, challenges persist regarding data quality and reusability.
Why should you join FAIRLYZ?
FAIRLYZ champions data sharing done right. The NIH-funded FAIRLYZ simplifies compliance with the NIH data sharing policy, allowing researchers to plan their studies early and easily publicize them. FAIRLYZ helps scientists with data find collaborators and funders. Data contributors can ask for funding to invest in data cleaning and curation or a for a new study that reuses their data. Data consumers will find data they can reuse while building collaborations for a new study. FAIRLYZ validates responsible data sharing practices through data quality control (QC). There is no need to share the data publicly if the data is protected, as the FAIRLYZ QC tool runs in the researcher’s compute environment. FAIRLYZ does not store data, it only stores metadata and QC data analysis results. FAIRLYZ empowers researchers from concept to publication, streamlining data management, study execution, and results sharing. It advocates for dedicated funding opportunities for publications arising from data sharing and data reuse. Its mission enables a scientific community to promote data reproducibility, foster inclusion and build trust. In medicine, by helping to grow the sample size of a bioinformatics, meta-analysis or AI/ML study, FAIRLYZ speeds up the journey from lab discoveries to life-saving treatments, bringing faster advancements to healthcare.
The acronym FAIRLYZ is derived from the acronyms FAIR and anaLYZable. FAIRLYZ follows FAIR data principles, by evaluating and supporting data that is Findable, Accessible, Interoperable, Reusable, and adds anaLYZable as the 5th principle. FAIRLYZ was developed through funding from a National Institute of Health NIAID contract.